Active control of disturbances, such as sound, vibration or disturbances in signals is well known. A recent review of the field is contained in `Active Sound Control` by P. A. Nelson and S. J. Elliot, Academic Press, 1991. Such systems use an actuator to generate a control disturbance which is out of phase with the original disturbance and so tends to cancel it. This technique is first described by Lueg in U.S. Pat. No. 2,043,416. Most active control systems use adaptive filtering techniques, in which the controller characteristic is adjusted according to an algorithm such as the `filtered-x LMS algorithm` such as disclosed by D. R. Morgan, IEEE Transactions on Acoustics, Speech and Signal Processing, Volume ASSF 28, Number 4, 1980, and by Widrow and Stearns, `Adaptive Signal Processing`, Prentice Hall, 1985. Two widely used techniques are feedforward control, as described in Chaplin U.S. Pat. No. 4,122,303, and feedback control as described in Ziegler U.S. Pat. No. 4,878,188.
In an active control system, the reference sensor is usually sensitive to the control disturbance. This provides a feedback mechanism which can cause the system to become unstable. One known method for compensating for this is to estimate the feedback component and to subtract it from the sensor signal. Both Chaplin and Ziegler use this compensation technique.
The adaptive feedforward controller disclosed in Chaplin is shown in FIG. 1. In this configuration the control system is used for canceling noise (1) propagating down a pipe or duct (2). An upstream (relative to the direction of sound propagation) or reference sensor (3) provides a reference signal (4) related to the sound at the sensor position. This signal is input to the control system (5) which in turn generates a control signal (6). The control signal is supplied to actuator (7) which in turn produces sound to cancel the original noise. An error or residual sensor (8), downstream of the actuator, produces a residual signal (9) related to the residual sound at that position. This signal is used to adjust the characteristic of the control system (5). The control system comprises a compensation filter (10) which acts on the control signal (6) to produce a compensation signal (11) which is an estimate of the component of signal (4) due to the actuator. Hence the characteristic of the filter should correspond to the impulse response of the physical system from controller output to controller input (including the response of the actuator (7), the sensor (3) and, for digital systems, any anti-aliasing filter or anti-imaging filter). The compensation signal (11) is subtracted at (12) from the reference signal (4) to produce an input signal (13). The input signal is then passed through a cancellation filter (14) to produce the control signal (6). The filtered-x LMS algorithm is commonly used to adjust the characteristic of the cancellation filter (14). The characteristic of compensation filter (10) can be determined by known system identification techniques.
The adaptive feedback controller disclosed by Ziegler is shown in FIG. 2. In this configuration the control system is used for canceling noise (1) propagating down a pipe or duct (2). A sensor (8), downstream of the actuator (relative to the direction of sound propagation), provides a signal (9) related to the sound at the sensor position. This signal is input to the control system (15) which in turn generates a control signal (6).degree. The control signal is supplied to actuator (7) which in turn produces sound to cancel the original noise. The same sensor (8) acts as a residual sensor since the signal (9) is related to the residual sound at that position. This signal is used to adjust the characteristic of the control system (15). The control system comprises a compensation filter (16) which acts on the control signal (6) to produce a compensation signal (17) which is an estimate of the component of signal (9) due to the actuator. Hence the characteristic of the filter should correspond to the impulse response of the physical system from controller output to controller input (including the response of the actuator (7), the sensor (8) and, for digital systems, any anti-aliasing filter or anti-imaging filter). The compensation signal (17) is subtracted at (18) from the residual signal (9) to produce an input signal (19). The input signal is then passed through a cancellation filter (20) to produce the control signal (6). The filtered-x LMS algorithm is commonly used to adjust the characteristic of the cancellation filter (20).
The performance of a feedforward control system is limited by noise at the reference sensor which is uncorrelated with the disturbance. This is called the `coherence limit`. The performance of a feedback control system is limited by the delay in the control loop, which limits performance to narrow-band or low frequency disturbances. Hence for disturbances which are a mixture of broadband and narrow band noise there is an advantage to be gained by using a combination of feedforward and feedback control.
This has been recognized by N. J. Doelman, `A Unified Strategy for the Active Reduction of Sound and Vibration`, Journal of Intelligent Materials Systems and Structures, Volume 2, Number 4 October 1991, pp. 558-580. This system is shown in FIG. 3 (also Doelman's FIG. 3). The outputs of a feedforward filter (5) and a feedback filter (15) are combined at (21) to produce the control signal (6). Doelman uses recursive filters and derives the optimal filter characteristics for stationary noise signals. However, there is no interaction between the two filters (5) and (15) in his arrangement. This can have serious implications since there is no guarantee that the filters he derives are stable. For an `off-line` design process the stability of the filters (both in open-loop and in closed loop) can be checked before the filter is implemented, but for adaptive control systems it is not practical to continually check for system stability. The risk of instability in the system would make this system unsuitable for practical implementation.
There is, therefore, a need for an adaptive control system which can be adapted easily without the risk of instability.